Big data is the term used to describe massive volumes of both structured and unstructured data that are so large that they are difficult to process using traditional database and data processing techniques. However, big data is becoming increasingly important, as the volume of data available grows rapidly due to the near ubiquitous availability of the Internet and data-generating devices, such as mobile phones and tablet computers. In addition, with the rapid growth of big data has come the recognition that the analysis of larger data sets can lead to more accurate analysis.
A particular challenge when dealing with vast amounts of data involves the visualization of this data and the interrogation of such visualizations for analysis purposes. Generating visualizations of such vast amounts of data will typically require a significant amount of processing power, otherwise the time taken to generate a visualization will be too long to be of sufficient use. This is especially problematic when dynamic interrogation of a visualization is necessary in order for a worthwhile analysis to be performed. Consequently, the generation of visualizations of vast amounts of data is far from trivial, and increasing the efficiency with which a visualization is generated and the efficiency with which a visualization can be dynamically interrogated is highly desirable and technically challenging.
This is particularly true for data sets that contain an extremely large number of co-ordinate records, wherein each co-ordinate record identifies a point using a set of co-ordinates that is indicative of the location of an incident. The processing and analysis of such co-ordinate data can therefore be extremely useful for identifying trends and/or for identifying anomalies in the expected patterns of incidents, both of which can be indicative of an issue or can identify an area of significance.